Scalable and exhaustive screening of metabolic functions carried out by microbial consortia

Abstract

MotivationThe selection of species exhibiting metabolic behaviors of interest is a challenging step when switching from the investigation of a large microbiota to the study of functions effectiveness. Approaches based on a compartmentalized framework are not scalable. The output of scalable approaches based on a non-compartmentalized modeling may be so large that it has neither been explored nor handled so far.ResultsWe present the Miscoto tool to facilitate the selection of a community optimizing a desired function in a microbiome by reporting several possibilities which can be then sorted according to biological criteria. Communities are exhaustively identified using logical programming and by combining the non-compartmentalized and the compartmentalized frameworks. The benchmarking of 4.9 million metabolic functions associated with the Human Microbiome Project, shows that Miscoto is suited to screen and classify metabolic producibility in terms of feasibility, functional redundancy and cooperation processes involved. As an illustration of a host-microbial system, screening the Recon 2.2 human metabolism highlights the role of different consortia within a family of 773 intestinal bacteria.Availability and implementationMiscoto source code, instructions for use and examples are available at: https://github.com/cfrioux/miscoto.

Metabolic Complementarity Between a Brown Alga and Associated Cultivable Bacteria Provide Indications of Beneficial Interactions

“…As a tool to further explore such interactions, Frioux et al (2018) have proposed the pipeline MiSCoTo. Given the metabolic networks of a host and several symbionts, this tool predicts potential metabolic capacities of one partner that could be unlocked by a contribution of a metabolite from another (e.g., the provision of carbohydrates by a photosynthetic organism unlocking the biochemical processes related to primary metabolism in heterotrophs).…”

Metage2Metabo: metabolic complementarity applied to genomes of large-scale microbiotas for the identification of keystone species

“…The network expansion algorithm computes the scope of a metabolic network from a description of the growth medium (seeds), that is, the family of metabolic compounds which are reachable according to a boolean abstraction of the network dynamics assuming that cycles cannot be self-activated. This algorithm has been widely used to analyse and refine metabolic networks [35,31,10,45,43], including for microbiota analysis [9,38,39,18]. As the scope ignores the stoichiometry of metabolites involved in reactions, it appears to be a good trade-off between the accuracy of metabolic predictions and the precision required for the input data.…”

“…Metabolic capabilities of the whole microbiota [18] can be computed using the network expansion algorithm with the command m2m cscope. This simulates the sharing of metabolic biosynthesis through a meta-organism composed of all GSMNs, and assesses the metabolic compounds that it can reach.…”

“…The targets can be the added-value of cooperation as presented above, or subcategories of them as defined by the user. This step relies on a functionality of MiSCoTo [18] to propose a minimal community of organisms suitable for the objective. M2m mincom assumes that all metabolic transports have equal costs as transport reactions are not well identified in automatically reconstructed GSMNs.…”

“…M2m mincom assumes that all metabolic transports have equal costs as transport reactions are not well identified in automatically reconstructed GSMNs. Further analysis introducing different costs based on additional knowledge on transport reactions can be performed by using the MiSCoTo package [18].…”